[unreadable] [unreadable] Conventional ROC analysis has been widely accepted as a standard in the assessment of diagnostic techniques for binary diagnoses. Many medical diagnoses, however, involve multiple diagnostic alternatives. Examples are breast cancer diagnosis using mammography, where the diagnostic classes are normal, benign, or malignant tumor, and cardiac disease diagnosis using myocardial perfusion SPECT (MRS), where the classes are normal, reversible or fixed defect. To assess multi-class diagnostic techniques, multi-class ROC is required, but has remained an unsolved problem ever since the introduction of binary ROC analysis in the 1950s. Sparked by a practical challenge raised by MPS optimization, the candidate proposed a three- class ROC analysis method that extends and unifies the decision theoretic, linear discriminant analysis and probabilistic foundations of binary ROC in a three-class paradigm. She has conducted five preliminary studies on three-class ROC analysis: (1) deriving its decision model [He, Metz, et.al IEEE Trans Med Imag (TMI) vol. 25(5), 2006]; (2) investigating its decision theoretic foundation [He and Frey, TMI, vol. 25(8), 2006]; (3) exploring its linear discriminant analysis (LDA) foundation [He and Frey, TMI, in press, 2006]; (4) establishing its probabilistic foundation; and (5) comparing it with conventional three-class LDA and revealing the limitations of conventional three-class LDA. The candidate obtained a PhD in Biomedical Engineering in December 2005 and had intensive training on medical imaging. She increased her interest in medical image quality assessment during the development of three-class ROC analysis; her knowledge of the statistics and decision theory principals used in this research is self-taught. Further exploring new areas opened by three- class ROC analysis requires systematic understanding of the statistical principles in decision theory, statistical learning, and Bayesian modeling, etc. Thus, she requests a two-year mentored phase focusing on formal biostatistics training. The training phase will substantially enhance the candidate's career development as an interdisciplinary investigator and contribute to her independent research to accomplish the following specific aims: 1) to establish the theoretical foundations of three-class ROC analysis; (2) to develop general statistical methods for three-class ROC analysis; (3) to apply the three-class methodologies to task-based medical image quality assessment. The significance of the proposed work is two-fold. First, it provides a rigorous solution to an open theoretical problem and will open new areas of theoretical research in ROC analysis and medical decision making. Second, it enables applications of task-based assessment techniques for multi-class diagnosis. These techniques have the potential to fundamentally improve current imaging techniques for disease detection and characterization, and thus to enhance doctors' performance in disease diagnosis, which will broadly benefit public health. [unreadable] [unreadable] [unreadable]